Conventionally, there has been proposed various devices for judging whether an apparatus is operating normally or not by using sound waves or vibrations generated by the operation of the apparatus. In such devices, in order to detect sound waves or vibrations generated from the apparatus, an signal input unit having a sensor (transducer) such as a sound detector is provided to convert the sounds or the vibrations into electrical signals and the converted signals is used as a target signal to be analyzed (see, e.g., Patent Reference 1).
In pulse sound detecting method of Patent Reference 1, in order to judge the presence of pulse sounds periodically generated by a rotation of the rotating body, waveform data (a set formed of an amplitude elements) obtained by sampling a waveform in a unit time from a target signal is compared with an amplitude threshold. If the number of elements exceeding the amplitude threshold is greater than a threshold value, it is judged that the sound waves generated by the apparatus contain periodic pulse sounds. The amplitude threshold is determined by multiplying the root-mean-square of the elements of the waveform data by a multiplier.
In Patent Reference 1, the apparatus is judged to be abnormal if pulse sounds are contained. However, there are many kinds of anomalies in the apparatus which can not be simply detected by the existence of pulse sounds. Therefore, in order to judging the presence of anomaly in more complex phenomena, an anomaly monitoring device has been proposed, which determines whether an apparatus is operating normally or not by extracting an amount of characteristics formed of a plurality of parameters extracting from a target signal generated by the operation of the apparatus and then classifying a distribution pattern of parameters in the amount of characteristics by a neural network (neuro computer) or fuzzy logic.
As for a neural network for classifying the distribution pattern of parameters in the amount of characteristics, it has been proposed to employ the competitive learning neural network (Self-Organizing Map (SOM)). The competitive learning neural network is a neural network having two layers, i.e., an input layer and an output layer, and having two modes of operation, a training mode and a checking mode.
In the training mode, training samples are given to the network, which is trained using an unsupervised learning scheme. If training samples are assigned with categories, neurons of the output layer can be associated with the categories and clusters each including neurons of a like category can be formed. Therefore, in the training mode, a clustering map representing categories can be matched to clusters of neurons in the output layer.
In the checking mode, an amount of characteristics (input data) to be classified is given to the competitive learning neural network which completed the training process and the category of a cluster to which an excited neuron belong to is mapped with the clustering map so that the category of the input data can be classified (see, e.g., Patent Reference 2).
[Patent Reference 1]
    Japanese Patent Laid-open Application No. 2002-71447.[Patent Reference 2]    Japanese Patent Laid-open Application No. 2004-354111.
As aforementioned, converting sound waves or vibrations generated from an apparatus to an electrical target signal is required to be done in the signal input unit to judge whether the apparatus is operating normally or not. Therefore, if the signal input unit malfunctions, it cannot be determined whether the apparatus is operating normally or not. Especially, if the apparatus is being monitored while the signal input unit malfunctions, occurrence of crucial failure in the apparatus may not be detected.